A method and system for controlling a display enables the sponsor of an advertisement to score displayed leads by either swiping left of right in the area of a touchscreen where the lead is displayed, to move a slider to indicate a value of the lead, or to otherwise input a score or rating. The method and system also allows a user to verify the accuracy of a displayed lead type identification, and to submit a corrected lead type identification to the provider if the initial identification is incorrect, for use as input to a neural network that performs the lead type identification.

This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

A system and method is provided for determining a portfolio of promotions from across a diverse mix of promotion categories. The diverse promotion portfolio may represent an inventory of promotions that are available for presenting to a consumer in a particular market. Also, a system and method is provided for determining a diverse group of promotions for presenting to a consumer. The diverse group of promotions may be determined based on whether a same or similar promotion was previously presented to the consumer.

Embodiments provide computer apparatuses, computer systems and computer-executable methods for generating geographic zone information associated with a consumer. A method includes receiving a first geographic location associated with the consumer, and programmatically identifying a first geographic zone associated with the first geographic location. The method also includes programmatically generating a first importance score associated with the first geographic zone and associated with the consumer. The method also includes storing, on a non-transitory computer-readable medium, the first geographic zone and the first importance score associated with the consumer.

Systems, methods, and devices for dynamically including advertisements in content sent to a user device. A server computing device deployed in a service provider network may select an advertisement from one of a plurality of advertisement decision services (ADSs), generate a thumbnail of the selected advertisement, and compare the generated thumbnail to previously generated thumbnails to determine whether the selected advertisement was previously sent to the user device within a time period. The server may also determine whether the number of times that the selected advertisement has been sent to the user device within the time period exceeds a threshold value, and include the selected advertisement in the content sent the user device in response to determining that the number of times that the selected advertisement has been sent to the user device within the time period does not exceed the threshold value.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing application program interface calls.

In some implementations, a computing device may determine, from multiple data sources, multiple event timelines, with each event timeline associated with a customer. Each event in an event timeline represents an interaction between the customer and a vendor of goods and/or services. For N (N>1) marketing campaigns, N augmented timelines may be created for each timeline by augmenting each event timeline with the individual marketing campaigns. Thus, for M (M>1) customers, MN augmented event timelines may be created. A trained machine learning model may perform an analysis of each augmented event timeline to predict results of executing each marketing campaign. The results may include total predicted revenue and total predicted cost resulting from executing each marketing campaign. A particular marketing campaign from the N marketing campaigns may be selected and execution of one or more marketing events may be initiated.

Techniques for using attention events for audience expansion are provided. In one technique, first interaction data that indicates multiple interactions by the first entity with multiple content items is stored. The interactions includes an interaction that is based on an amount of time that content within one of the content items was presented to the first entity. Based on the first interaction data, similarity data that identifies one or more content delivery campaigns that are similar to a particular content delivery campaign is generated. Second interaction data that indicates interaction(s) by a second entity with content item(s) is stored. Based on the second interaction data and the similarity data, association data that associates the second entity with the particular content delivery campaign is stored. The association data may be used to identify the particular campaign in response to receiving a content request from a computing device of the second entity.

The presented technology is directed to devices, systems, and their methods of use for evaluating and optimizing the effectiveness of digital advertisements to increase sales such as in a digital ad-buying platform. In particular, the presented platform is configured for determining incremental lift of a product due to an advertisement and/or a promotion that is distributed online. Presented herein, therefore, are devices, systems, and methods of monitoring, measuring, and/or optimizing lift of an advertising campaign, such as a real-time, digital ad buying campaign implemented by a suitably configured advertising platform.

Systems and methods for managing feedback for a multi-ARM bandit algorithm are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include sending, to a device, a creative configured for display on the device. The operations may include receiving, from the device, feedback data associated with the creative, wherein the feedback data indicates a degree of user interaction with the creative. The operations may include checking the feedback data for a baked time associated with the creative. The operations may include sending the feedback data to a node based on the baked time. The operations may include updating, by the node, a model associated with the feedback data.